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Alex Preston

e have come to the end of privacy; our private lives, as our grandparents would have recognised them, have been winnowed away to the realm of the shameful and secret. To quote ex-tabloid hack Paul McMullan, "privacy is for paedos". Insidiously, through small concessions that only mounted up over time, we have signed away rights and privileges that other generations fought for, undermining the very cornerstones of our personalities in the process. While outposts of civilisation fight pyrrhic battles, unplugging themselves from the web - "going dark" - the rest of us have come to accept that the majority of our social, financial and even sexual interactions take place over the internet and that someone, somewhere, whether state, press or corporation, is watching.

The past few years have brought an avalanche of news about the extent to which our communications are being monitored: WikiLeaks, the phone-hacking scandal, the Snowden files. Uproar greeted revelations about Facebook's "emotional contagion" experiment (where it tweaked mathematical formulae driving the news feeds of 700,000 of its members in order to prompt different emotional responses). Cesar A Hidalgo of the Massachusetts Institute of Technology described the Facebook news feed as "like a sausage... Everyone eats it, even though nobody knows how it is made".

Sitting behind the outrage was a particularly modern form of disquiet - the knowledge that we are being manipulated, surveyed, rendered and that the intelligence behind this is artificial as well as human. Everything we do on the web, from our social media interactions to our shopping on Amazon, to our Netflix selections, is driven by complex mathematical formulae that are invisible and arcane.

Most recently, campaigners' anger has turned upon the so-called Drip (Data Retention and Investigatory Powers) bill in the UK, which will see internet and telephone companies forced to retain and store their customers' communications (and provide access to this data to police, government and up to 600 public bodies). Every week, it seems, brings a new furore over corporations - Apple, Google, Facebook - sidling into the private sphere. Often, it's unclear whether the companies act brazenly because our governments play so fast and loose with their citizens' privacy ("If you have nothing to hide, you've nothing to fear," William Hague famously intoned); or if governments see corporations feasting upon the private lives of their users and have taken this as a licence to snoop, pry, survey.

We, the public, have looked on, at first horrified, then cynical, then bored by the revelations, by the well-meaning but seemingly useless protests. But what is the personal and psychological impact of this loss of privacy? What legal protection is afforded to those wishing to defend themselves against intrusion? Is it too late to stem the tide now that scenes from science fiction have become part of the fabric of our everyday world?

Novels have long been the province of the great What If?, allowing us to see the ramifications from present events extending into the murky future. As long ago as 1921, Yevgeny Zamyatin imagined One State, the transparent society of his dystopian novel, We. For Orwell, Huxley, Bradbury, Atwood and many others, the loss of privacy was one of the establishing nightmares of the totalitarian future. Dave Eggers's 2013 novel paints a portrait of an America without privacy, where a vast, internet-based, multimedia empire surveys and controls the lives of its people, relying on strict adherence to its motto: "Secrets are lies, sharing is caring, and privacy is theft." We watch as the heroine, Mae, disintegrates under the pressure of scrutiny, finally becoming one of the faceless, obedient hordes. A contemporary (and because of this, even more chilling) account of life lived in the glare of the privacy-free internet is Nikesh Shukla's , which charts the existence of a lonely writer whose only escape is into the shallows of the web. "The first and last thing I do every day," the book begins, "is see what strangers are saying about me."

Our age has seen an almost complete conflation of the previously separate spheres of the private and the secret. A taint of shame has crept over from the secret into the private so that anything that is kept from the public gaze is perceived as suspect. This, I think, is why defecation is so often used as an example of the private sphere. Sex and shitting were the only actions that the authorities in Zamyatin's One State permitted to take place in private, and these remain the battlegrounds of the privacy debate almost a century later. A rather prim leaked memo from a GCHQ operative monitoring Yahoo webcams notes that "a surprising number of people use webcam conversations to show intimate parts of their body to the other person".

It is to the bathroom that Max Mosley turns when we speak about his own campaign for privacy. "The need for a private life is something that is completely subjective," he tells me. "You either would mind somebody publishing a film of you doing your ablutions in the morning or you wouldn't. Personally I would and I think most people would." In 2008, Mosley's "sick Nazi orgy", as the News of the World glossed it, featured in photographs published first in the pages of the tabloid and then across the internet. Mosley's defence argued, successfully, that the romp involved nothing more than a "standard S&M prison scenario" and the former president of the FIA won £60,000 damages under Article 8 of the European Convention on Human Rights. Now he has rounded on Google and the continued presence of both photographs and allegations on websites accessed via the company's search engine. If you type "Max Mosley" into Google, the eager autocomplete presents you with "video," "case", "scandal" and "with prostitutes". Half-way down the first page of the search we find a link to a professional-looking YouTube video montage of the NotW story, with no acknowledgment that the claims were later disproved. I watch it several times. I feel a bit grubby.

"The moment the Nazi element of the case fell apart," Mosley tells me, "which it did immediately, because it was a lie, any claim for public interest also fell apart."

Here we have a clear example of the blurred lines between secrecy and privacy. Mosley believed that what he chose to do in his private life, even if it included whips and nipple-clamps, should remain just that - private. The News of the World, on the other hand, thought it had uncovered a shameful secret that, given Mosley's professional position, justified publication. There is a momentary tremor in Mosley's otherwise fluid delivery as he speaks about the sense of invasion. "Your privacy or your private life belongs to you. Some of it you may choose to make available, some of it should be made available, because it's in the public interest to make it known. The rest should be yours alone. And if anyone takes it from you, that's theft and it's the same as the theft of property."

Mosley has scored some recent successes, notably in continental Europe, where he has found a culture more suspicious of Google's sweeping powers than in Britain or, particularly, the US. Courts in France and then, interestingly, Germany, ordered Google to remove pictures of the orgy permanently, with far-reaching consequences for the company. Google is appealing against the rulings, seeing it as absurd that "providers are required to monitor even the smallest components of content they transmit or store for their users". But Mosley last week extended his action to the UK, filing a claim in the high court in London.

Mosley's willingness to continue fighting, even when he knows that it means keeping alive the image of his white, septuagenarian buttocks in the minds (if not on the computers) of the public, seems impressively principled. He has fallen victim to what is known as the Streisand Effect, where his very attempt to hide information about himself has led to its proliferation (in 2003 Barbra Streisand tried to stop people taking pictures of her Malibu home, ensuring photos were posted far and wide). Despite this, he continues to battle - both in court, in the media and by directly confronting the websites that continue to display the pictures. It is as if he is using that initial stab of shame, turning it against those who sought to humiliate him. It is noticeable that, having been accused of fetishising one dark period of German history, he uses another to attack Google. "I think, because of the Stasi," he says, "the Germans can understand that there isn't a huge difference between the state watching everything you do and Google watching everything you do. Except that, in most European countries, the state tends to be an elected body, whereas Google isn't. There's not a lot of difference between the actions of the government of East Germany and the actions of Google."

All this brings us to some fundamental questions about the role of search engines. Is Google the de facto librarian of the internet, given that it is estimated to handle 40% of all traffic? Is it something more than a librarian, since its algorithms carefully (and with increasing use of your personal data) select the sites it wants you to view? To what extent can Google be held responsible for the content it puts before us?

It isn't Mosley who has pushed European courts into giving a definitive answer to these questions, but, rather, an unknown lawyer from northwest Spain. In 2009, Mario Costeja González found that a Google search of his name brought up a 36-word document concerning a case from the late 90s in which banks threatened to seize his home. The information was factually incorrect - he'd actually paid off the debts in question. More than this, it was, he argued, irrelevant. He is now a lawyer with a successful practice and any former money worries ought not to feature on the internet record of his life. Google fought the case within Spain and then all the way to the European Court of Justice. Costeja González won, the article was taken down, the victory labelled "the right to be forgotten".

Google's response to the ruling has been swift and sweeping, with 70,000 requests to remove information processed in the weeks following the judgment. A message now appears at the bottom of every search carried out on Google, warning: "Some results may have been removed under data protection law in Europe." It seems that Google has not been judging the quality of these requests and relying on others to highlight content that has been taken down erroneously. Search results to newspaper articles on Dougie McDonald, a Scottish football referee accused of lying, were taken down. And a Robert Peston piece for the BBC about Merrill Lynch CEO Stan O'Neal's role in the 2008 financial crisis was removed; as of August 2014, it is still missing from Google searches.

To understand how much protection the law offers those wishing to defend their privacy against the triumvirate of state, press and data-harvesting corporations, I turn to one of the country's top privacy lawyers, Ruth Collard at Carter Ruck. We meet in Carter Ruck's offices near St Paul's Cathedral. I sit under the beady gaze of the firm's late founder - the target of much Private Eye bile - until Collard, more genial than I'd expected, with short, neat hair, comes to meet me. We stroll to a coffee shop and I ask her about the Costeja González case.

"I think it's a very surprising decision, I really do," she says, "but it was Google's collection of data, the arrangement and prioritisation of it that influenced the judgment, as the court found they were a 'controller' of the information."

I ask about the freedom of speech implications of the judgment - surely every politician will be applying to Google, trying to scrub away the traces of the sordid affair, the expenses scandal? She nods emphatically. "Almost immediately after the judgment, there were reports of a politician trying to clean up his past. We have been contacted by clients who have read about the judgment and are interested. So far, the ones that have contacted me, I have not thought they had a case to make. It was clear from the judgment that a balance has to be struck - between the interest of the subject in keeping information private and the interest of internet users in having access to it. The effect will be removing information that may have been once completely justified in being there, but is now outdated or irrelevant."

We go on to discuss the changing face of privacy in the internet age. She firstly notes how relatively young privacy law is in this country - only since 1998 have there been laws in place to protect privacy. Cases brought before this, such as that of 'Allo 'Allo star Gordon Kaye, who in 1990 was photographed in his hospital bed badly battered following a car crash, had to prove that a breach of confidence had taken place. Collard points me to the recent Paul Weller case, in which the Modfather sued after being pictured in the Daily Mail out walking with his 16-year-old daughter and 10-month-old twins.

"A lot of these cases are Mail cases," she says with a twinkle. "It was the fact that the photos showed the children's faces that was found to be significant by the court. Weller brought the claim on behalf of all three children and it succeeded [although the Mail is appealing]." In his judgment, Justice Dingemans argued that the children's faces represented "one of the chief attributes of their respective personalities... These were photographs showing the expressions on faces of children, on a family afternoon out with their father. Publishing photographs of the children's faces, and the range of emotions that were displayed, and identifying them by surname, was an important engagement of their Article 8 rights."

Collard tells me: "The Mail argued very hard on a number of grounds, one of which was that the children didn't have a reasonable expectation of privacy, given that the teenager a couple of years earlier had done some modelling for Teen Vogue and the babies had had photos of them tweeted by their mother. However, she had been careful when tweeting not to show their faces. This thing about facial expressions is new and it will be interesting to see where it goes."

This year has not only seen a spate of news stories surrounding the issue of privacy, but also a series of literary and artistic approaches to the subject. One of the most interesting was , a play at the Donmar Warehouse that came out of a close collaboration between director Josie Rourke and playwright James Graham. It was a groundbreaking production, from the moment the audience were urged to leave their telephones on as they entered the theatre. Over the course of the play, by turn intrusively intimate and angrily political, the audience (and their smartphones) found themselves at the centre of an attempt to chart the status of contemporary privacy - and the seemingly muted public reaction to its loss.

Within minutes of entering the theatre, I was squirming in my seat. An actor on stage was publicly analysing the results of a study designed by the psychometrics department of Cambridge University that used my Facebook profile to reveal my innermost secrets, questioning almost every aspect of my personality, from my political views to my sexuality. It was terrifying and compelling all at once. As the play ratcheted up to a coruscating finale, we, the audience, were made to see the enormous value of the rights we'd handed over as the mere cost of life in the 21st century (who, we were asked, had read the iTunes privacy policy? It's word for word as long as The Tempest).

I meet Rourke and Graham in Donmar's offices on Dryden Street in Covent Garden. We discuss the play, how charged tremors ran around the audience after each subsequent revelation and, above all, the sense of outrage that propels the narrative as it turns its fire on Facebook, Apple and finally on the American government. Are they worried, I ask, that there seems to be so little anger on behalf of the public? That it is beginning to feel (to me at least) as if we have given up our right to a private life with barely a whimper? Graham nods. "I think a lot of normal people, even if they did have the knowledge of how much they're publishing and how much they're giving away, and how much a corporate or government agency might know about them, would just go, 'That's fine, because I'm not interesting enough to be spied on.'"

There were moments in the play that were profoundly uncomfortable: for the audience and, I would imagine, for Rourke and Graham. Both director and playwright featured on stage and we delved deeply into their private lives, into their secret selves (and into our own). I ask about the dynamic between the two of them during the writing of the play and where they decided to draw the line between probing and invasive.

"We thought it through," Graham answers. "From the very beginning, we decided that we never wanted any of these interactions to be exposing or judgmental. We wanted them to be kind and benevolent and ask people to examine their data streams."

Rourke nods in agreement. "In order to research the show, we kept pushing at the boundaries of each other's privacy, really to see what that felt like. I don't want to make it sound like a piece of performance art, but we kept asking ourselves - oh look, we've found out that it's possible to do this. Are we prepared to do that to each other? The distinction between secret and private has been the guiding philosophical principle. We're not looking to get your secrets. We're asking you to test this thing which is less tangible and less transactable, which is your privacy."

A few days after our meeting, Rourke puts me in touch with Michal Kosinksi, the Cambridge academic who, with David Stillwell, has designed youarewhatyoulike.com, the psychometric algorithm that produces from your Facebook "likes" a map of your soul. I think of Ruskin, who in 1864 said: "Tell me what you like and I'll tell you what you are." I also think of Orwell's thought police and Philip K Dick's The Minority Report, where criminals are identified and arrested before they commit crimes. This is one of the problems about advances in technology: we are preconditioned to view them through a dystopian lens, with Orwell, Ballard, Burgess and others staring over our shoulders, marvelling, but fearful.

While I wait for my results, I ask Kosinski whether the potential for misuse worries him. "Most technologies have their bright and dark side," he replies, buoyantly. "My personal opinion is that a machine's ability to better understand us would lead to improved consumer experience, products, etc... But imagine that we published a clone of youarewhatyoulike.com that simply predicted which of your friends was gay (or Christian or liberal or HIV-positive, etc); lynches are not unlikely to follow..."

I'm left baffled by my results. According to the algorithm, I'm 26 (I'm actually 35). The program is unsure whether I'm male or female (I'm male). I'm borderline gay or, as Kosinski puts it in his analysis: "You are not yet gay, but very close." I'm most likely to be single, extremely unlikely to be married (I'm not sure what my wife will say to all this). The algorithm correctly predicts my professional life (art, journalism, psychology) and my politics (liberal) but claims that I exhibit low neuroticism. It should sit next to me on a turbulent flight. I realise that I'm viewing the results of a kind of double of myself, the public persona I present through social media (and over which I presume some sort of control), nothing like the real me. For that, I need a psychiatrist.

Alongside Rourke, Graham, William Hague and director of Liberty, Shami Chakrabarti, one of the real-world figures represented in the play Privacy is academic and psychoanalyst Josh Cohen. Cohen's book, The Private Life (2013), is an intelligent and highly literary exploration of the changing nature of privacy in the age of Facebook and Celebrity Big Brother. Skipping from Katie Price to Freud to Booker-winning author Lydia Davis, Cohen paints a convincing picture of a culture fighting a desperate psychological battle over the private self. He argues that both our ravenous hunger for celebrity gossip and the relentless attempts by the wealthy to protect their privacy have recast the private life as "a source of shame and disgust". The tabloid exposé and the superinjunction both "tacitly accede to the reduction of private life to the dirty secrets hidden behind the door".

And yet what neither the press nor the lawyers recognise when they treat privacy as they would secrecy - as something that can be revealed, possessed, passed on - is that the truly private has a habit of staying that way. Cohen argues that the private self is by definition unknowable, what George Eliot calls "the unmapped country within us". In an email conversation, Cohen gives me a condensation of this thesis: "When we seek to intrude on the other's privacy, whether with a telephoto lens, a hacking device or our own two eyes, we're gripped by the fantasy of seeing their most concealed, invisible self. But the frustration and disappointment is that we only ever get a photograph of the other, an image of their visible self - a mere shadow of the true substance we really wanted to see. The most private self is like the photographic negative that's erased when exposed to the light."

There is something strangely uplifting in this idea - that no matter how deep they delve, the organs of surveillance will never know my true self, for it is hidden even from me.

I ask Cohen about the differences between our "real" selves and those we project online. I think of the younger, gayer, less neurotic incarnation of myself that appears on Facebook. "I agree that the online persona has become a kind of double," he says. "But where in Dostoevsky or Poe the protagonist experiences his double as a terrifying embodiment of his own otherness (and especially his own voraciousness and destructiveness), we barely notice the difference between ourselves and our online double. I think most users of social media and YouTube would simply see themselves as creating a partial, perhaps preferred version of themselves."

I remember something James Graham had said to me: that early on in the writing of Privacy, he'd sat at Josie Rourke's kitchen table and read his tweets out loud to her. "I had to stop it was so awful... Just seeing the person there, listening as I said what I thought about my food, about politics, about what was on the television, it felt very exposing, like I'd sold myself badly."

This is the horror of social media - that it gives us the impression we are in control of our virtual identities, putting out messages that chime with our "real" selves (or some idealised version of them). In fact, there is always slippage and leakage, the subconscious asserting its obscure power. The internet can, as Cohen tells me, "provide a way of exploring and playing the multiplicity and complexity of the self". It can also prove to us just how little control we have over how we appear. As William Boyd put it in Brazzaville Beach: "The last thing we discover in life is our effect."

There is, of course, a flipside to the dystopian view of profit-hungry corporations and totalitarian governments relentlessly reaping our private selves. Josh Cohen describes the lifelogging movement as bearing "an overriding tone of utopian enthusiasm". Lifelogging involves the minute-by-minute transmission of data about one's life, whether by photographs, web-journals or the sort of Quantified Self technologies - wearable watches, data-gathering smartphone apps - developed by German firm Datarella (and many others, Google Glass not least among them). An early proponent of lifelogging was conceptual artist Alberto Frigo, who in 2003 decided to record every object he would hold with his right hand for the next 36 years. He put the pictures on his website, 2004-2040.com.

Frigo's project started with photographs but has developed into a labyrinthine mapping of his thoughts and dreams, the music he is listening to and the world around him. The website is now a wormhole, a place in which it is possible to lose yourself in the beautiful but useless ephemera of a single existence. Frigo tells me that his aim is to create for a future audience "some kind of a Rosetta Stone of this time, where different aspects of a person's life, recorded with different media, can be compared and interpreted."

2004-2040.com has come to dominate Frigo's life. He has insisted on radical honesty, writing down detailed dreams, a sophisticated map of his mental state. For his wife, this was problematic. "She did not accept that I dreamed of other girls," he tells me. "This slowly led to our separation." It also led to a hiatus in Frigo's lifelogging, when he carried on in private, recording but not publishing his material. "My ex-wife did not wish me to publish my dreams and I did not feel like presenting my project without one part. After the divorce, it took me sometime before I went online again."

Josh Cohen told me about the psychic risks of lifelogging. For some, he said, "shadowing your transient, irretrievable life is a permanent digital life, and the really frightening spectre here is that the digital recording becomes more 'real', more authoritative than your memory." I asked Frigo about this. How would he look back on it once it had all finished? How will he feel to have this stunning, often baffling pictorial record of his life? "At the end of the project," he says, "I think I would feel that it is time to retire. I would love to go back to the Italian alps, when I am originally from, and have not so much to do with technology anymore, just have a little garden and look back, write a book perhaps, but mostly take care of a little land which I unfortunately never had the possibility to own as my father went off to Canada when I was little... I might well end up in a hospital connected to a machine for the rest of my life, who knows? Perhaps, in 2040 I might not reach my 60th birthday and the 1,000,000 photos of all the objects my right hand has used, the DNA code of my life, will remain incomplete, and no one will care about them or me."

Your privacy has a value. There are even companies such as RapLeaf.com that will tell you what your personal information is worth. The basic facts? Very little. More detailed information - for example, you own a smartphone, are trying to lose weight or planning a baby - are worth much more. Big life changes - marriage, moving home, divorce - bring with them fundamental changes in our buying patterns as we seek, through the brands with which we associate ourselves, to recast the narratives of our lives. Through analysis of buying patterns, US retailer Target predicted one of its customers was pregnant (and sent her coupons for maternity wear) before the teenager had broken the news to her disapproving parents.

Perhaps the reason people don't seem to mind that so much of their information is leaking from the private to the public sphere is not, as some would have it, that we are blind and docile, unable to see the complex web of commercial interests that surround us. Maybe it's that we understand very clearly the transaction. The internet is free and we wish to keep it that way, so corporations have worked out how to make money out of something we are willing to give them in return - our privacy. We have traded our privacy for the wealth of information the web delivers to us, the convenience of online shopping, the global village of social media.

Let me take you back to August 2006, the Chesterfield Hotel, Mayfair. My little brother (Preston from the Ordinary Boys) was marrying a girl he'd met on the telly (Chantelle Houghton). Celebrity Big Brother was still in its Channel 4 pomp, attracting 6 million viewers and upwards, the focus of water-cooler debate and gossip mag intrigue. The wedding was, briefly, an event. I remember a moment between the ceremony and the reception when we were queuing up in our gladrags to have our pictures taken for the OK! magazine spread. I felt a sudden, instinctive lurch - the thought of my phiz besmirching every hairdresser's salon and dentist's waiting room. I wasn't on Facebook, Twitter hadn't been invented, Friends Reunited? No thanks. I ran - to the bemusement of my family and the photographer.

Now, though, I post pictures of my breakfast on Instagram, unspool my soul in 140-character soundbites on Twitter, allow - even encourage - Facebook and Google and Apple to track my every move through the smartphone that has become less a piece of technology and more an extension of (and often a replacement for) my brain. I write articles on subjects I'd previously kept secret from my nearest and dearest. I let a Sunday newspaper take a (relatively tasteful) picture of me and my children when I was promoting my last novel. We have all - to a greater or lesser extent - made this same transaction and made it willingly (although my children didn't have much say in the matter).

We weren't private creatures in centuries past, either. In a 1968 talk on privacy in the electronic age, sociologist Marshall McLuhan argued that it was the coming of a new technology - books - and the "closed-off architecture" needed to read and study that had forged the sense of the private self. It may be that another new technology - the internet - is radically altering our sense of what (if anything) should remain private. We live in a liberal democracy, but, with recent lurches to the right, here and abroad, you don't need to be Philip K Dick to imagine the information you gave up so glibly being used against you by a Farage-led dictatorship.

More immediately, there is the normalising effect of surveillance. There is a barrier or check on our behaviour when we know we are being watched: deviancy needs privacy. This was the thinking behind Jeremy Bentham's Panopticon, a model for a jail where a single watching guard could survey a whole prison of inmates (the model, by the way, for Zamyatin's One State). Soon, it didn't matter whether the guard was on duty or not, the mere possibility of surveillance was enough to ensure compliance. This is where we find ourselves now, under surveillance that may seem benign enough but which nonetheless asserts a dark, controlling power over us, the watched.

The message seems to be that if you really want to keep something private, treat it as a secret, and in the age of algorithmic analysis and big data, perhaps best to follow Winston Smith's bitter lesson from Nineteen Eighty-Four: "If you want to keep a secret, you must also hide it from yourself."

Here lies our greatest risk, one insufficiently appreciated by those who so blithely accept the tentacles of corporation, press and state insinuating their way into the private sphere. As Don DeLillo says in Point Omega: "You need to know things the others don't know. It's what no one knows about you that allows you to know yourself." By denying ourselves access to our own inner worlds, we are stopping up the well of our imagination, that which raises us above the drudge and grind of mere survival, that which makes us human.

I asked Josh Cohen why we needed private lives. His answer was a rallying cry and a warning. "Privacy," he said, "precisely because it ensures we're never fully known to others or to ourselves, provides a shelter for imaginative freedom, curiosity and self-reflection. So to defend the private self is to defend the very possibility of creative and meaningful life."
Alex Preston's most recent novel is In Love and War, published by Faber at £14.99. To buy a copy for £11.99 with free UK p&p call 0330 333 6846 or go to guardianbookshop.co.uk Topics

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Alex Preston e have come to the end of privacy; our private lives, as our grandparents would have recognised them, have been winnowed away to the realm of the shameful and secret. To quote ex-tabloid hack Paul McMullan, "privacy is for paedos".

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DevSkiller - Powerful tool to test developers skills

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Computer programming is the very basis of the digital age that we live in today. Every time you like a post on social media, send an email, or set an alarm on your phone, a programming language is working behind the scenes - pulling the strings.

But where did it all begin? And what spurred its growth into the leading industry that exists today? Most of all, why is knowing the history of programming languages important to hiring developers?

Join us, as we embark on a tour of the history of programming languages. This retrospective will demonstrate how much computer programming has developed over the years. It'll take you back from the early languages and complicated machine code to sophisticated human-readable language that powers our favorite technologies today.

The first programming language

Did you know that the first programming language was invented way back in 1843? Ada Lovelace came up with the first-ever machine algorithm for an early computing machine that she wrote down on a piece of paper because no computers existed at the time! Programming languages have obviously come a long way since then but in order to understand the history of programming languages, one must first acknowledge their origin.

History of programming languages: A timeline

Listed below is a timeline of the history of programming languages. The first known programming languages were complicated machine codes that were manually inputted into early computing machines. As you'll discover, computer programming developed quickly from machine codes to fully automated human-readable code.

1843: Ada Lovelace's machine algorithm

Ada Lovelace invents the first-ever machine algorithm for Charles Babbage's Difference Machine that lays the foundation for all programming languages.

Source: Wikimedia Commons

1944-45: Plankalkül

Somewhere between 1944-45, Konrad Zuse developed the first 'real' programming language called Plankalkül (Plan Calculus). Zeus's language (among other things) allowed for the creations of procedures, which stored chunks of code that could be invoked over and over to perform routine operations.

Source: Wikimedia Commons

1949: Assembly Language

Assembly language was used in the Electronic Delay Storage Automatic Calculator (EDSAC). Assembly language was a type of low-level programming language that simplified the language of machine code. In other words, the specific instructions necessary to operate a computer.

1949: Shortcode

Shortcode (or Short-order code), was the first High-Level Language (HLL) suggested by John McCauley in 1949. However, it was William Schmitt who implemented it for the BINAC computer the same year and for the UNIVAC in 1950.

1952: Autocode

Autocode was a general term used for a family of programming languages. First developed by Alick Glennie for the Mark 1 computer at the University of Manchester, Autocode was the first-ever compiled language to be implemented meaning that it can be translated directly into machine code using a program called a compiler. Autocode was used on the Ferranti Pegasus and Sirius early computing machines in addition to the Mark 1.

1957: FORTRAN

FORmula TRANslation or FORTRAN was created by John Backus and is considered to be the oldest programming language in use today. The programming language was created for high-level scientific, mathematical, and statistical computations. FORTRAN is still in use today in some of the world's most advanced supercomputers.

Source: Flickr

1958: ALGOL (Algorithmic Language)

Algorithmic language or ALGOL was created by a joint committee of American and European computer scientists. ALGOL served as the starting point for the development of some of the most important programming languages including Pascal, C, C++, and Java.

1958: LISP (List Processor)

List processor or LISP was invented by John McCarthy at the Massachusetts Institue of Technology (MIT). Originally purposed for artificial intelligence, LISP is one of the oldest programming languages still in use today and can be used in the place of Ruby or Python. Companies such as Acceleration, Boeing, and Genworks are still using LISP in their tech stacks.

Source: WikiMedia

1959: COBOL (Common Business Oriented Language)

Common Business Oriented Language (COBOL), is the behind many credit card processors, ATMs, telephone and cell calls, hospital signals, and traffic signals systems (just to name a few). The development of the language was led by Dr. Grace Murray Hopper and was designed so that it could run on all brands and types of computers. COBOL is still used to this day primarily for banking and programming language gamification systems.

Source: Pixabay

1964: BASIC (Beginner's All-Purpose Symbolic Instruction Code)

Beginners All-Purpose Symbolic Instruction Code or BASIC was developed by a group of students at Dartmouth College. The language was written for students who did not have a strong understanding of mathematics or computers. The language was developed further by Microsoft founders Bill Gates and Paul Allen and became the first marketable product of the company.

1970: PASCAL

Named after the French mathematician Blaise Pascal, Niklaus Wirth developed the programming language in his honor. It was developed as a learning tool for computer programming which meant it was easy to learn. It was favored by Apple in the company's early days, because of its ease of use and power.

Source: Flickr

1972: Smalltalk

Developed at the Xerox Palo Alto Research Centre by Alan Kay, Adele Goldberg, and Dan Ingalls, Smalltalk allowed for computer programmers to modify code on the fly. It introduced a variety of programming language aspects that are visible languages of today such as Python, Java, and Ruby. Companies such as Leafly, Logitech, and CrowdStrike state they use Smalltalk in their tech stacks.

1972: C

Developed by Dennis Ritchie at the Bell Telephone Laboratories for use with the Unix operating system. It was called C because it was based on an earlier language called 'B'. Many of the current leading languages are derivatives of C including; C#, Java, JavaScript, Perl, PHP, and Python. It also has been/still being used by huge companies like Google, Facebook, and Apple.

1972: SQL (SEQUEL at the time)

SQL was first developed by IBM researchers Raymond Boyce and Donald Chamberlain. SEQUEL (as it was referred to at the time), is used for viewing and changing information that is stored in databases. Nowadays the language is an acronym - SQL, which stands for Structured Query Language. There are a plethora of companies that use SQL and some of them include Microsoft and Accenture.

1980/81: Ada

Ada was originally designed by a team led by Jean Ichbiah of CUU Honeywell Bull under contract to the United States Department of Defense. Named after the mid-19th-century mathematician Ada Lovelace, Ada is a structured, statically typed, imperative, wide-spectrum, and object-oriented high-level programming language. Ada was extended from other popular programming languages at the time such as Pascal. Ada is used for air-traffic management systems in countries such as Australia, Belgium, and Germany as well as a host of other transport and space projects.

1983: C++

Bjarne Stroustrup modified the C language at the Bell Labs, C++ is an extension of C with enhancements such as classes, virtual functions, and templates. It has been listed in the top 10 programming languages since 1986 and received Hall of Fame status in 2003. C++ is used in MS Office, Adobe Photoshop, game engines, and other high-performance software.

1983: Objective-C

Developed by Brad Cox and Tom Love, Objective-C is the main programming language used to write software for macOS and iOS, Apple's operating systems.

Source: Cleanpng

1987: Perl

Perl was created by Larry Wall and is a general-purpose, high-level programming language. It was originally designed as a scripting language designed for text editing but nowadays it's widely used for many purposes such as CGI, database applications, system administration, network programming, and graphic programming.

Source: Flickr

1990: Haskell

Haskell is a general-purpose programming language named after the American logician and mathematician Haskell Brooks Curry. It is a purely functional programming language meaning it's primarily mathematical. It's used across multiple industries particularly those that deal with complicated calculations, records, and number-crunching. Like many other programming languages from this era, it is not overly common to see Haskell in use for well-known applications. With that said, the programming language has been used to write a number of games one of which is Nikki and the Robots.

Source: Wikimedia

1991: Python

Named after the British comedy troupe 'Monty Python', Python was developed by Guido Van Rossum. It is a general-purpose, high-level programming language created to support a variety of programming styles and be fun to use (a number of the tutorials, samples, and instructions often contain Monty Python references). Python is, to this day, one of the most popular programming languages in the world is used by companies such as Google, yahoo, and Spotify.

Source: Flickr

1991: Visual Basic

Developed by Microsoft, Visual Basic allows programmers to utilize a drag-and-drop style of choosing and changing pre-selected chunks of code through a graphical user interface (GUI). The language is not overly used these days however Microsoft has used portions Visual Basic to a number of their applications like Word, Excel, and Access.

Source: Pixabay

1993: Ruby

Created by Yukihiro Matsumoto, Ruby is an interpreted high-level programming language. A teaching language which was influenced by Perl, Ada, Lisp, and Smalltalk - among others. Ruby's primary uses are for web applications development and Ruby on Rails. Twitter, Hulu, and Groupon are some well-known examples of companies that use Ruby.

Source: Wikimedia

1995: Java

Java is a general-purpose, high-level language created by James Gosling for an interactive TV project. It has cross-platform functionality and is consistently among the top of the world's most popular programming languages. Java can be found everywhere, from computers to smartphones to parking meters.

Source: Wikimedia

1995: PHP

Formerly known as 'Personal Home Page' which now stands for 'Hypertext Preprocessor', PHP was developed by Rasmus Lerdorf. Its primary uses include building and maintaining dynamic web pages, as well as server-side development. Some of the biggest companies from across the globe use PHP including Facebook, Wikipedia, Digg, WordPress, and Joomla.

1995: JavaScript

JavaScript was created by Brendan Eich, this language is primarily used for dynamic web development, PDF documents, web browsers, and desktop widgets. Almost every major website uses JavaScript. Gmail, Adobe Photoshop, and Mozilla Firefox include some well-known examples.

2000: C#

Developed at Microsoft with the hope of combining the computing ability of C++ with the simplicity of Visual Basic, C# is based on C++ and shares many similarities with Java. The language is used in almost all Microsoft products and is seen primarily in developing desktop applications.

2003: Scala

Developed by Martin Odersky, Scala which combines mathematical functional programming and organized object-oriented programming. Scala's compatibility with Java makes it helpful with Android development. Linkedin, Twitter, Foursquare, and Netflix are just a few examples of the many companies that use Scala in their tech stacks.

2003: Groovy

Derived from Java, Groovy was developed by James Strachan and Bob McWhirter. The language improves productivity because of its succinct and easy to learn. Some well-known companies that are using Groovy in their tech stacks are Starbucks, Transferwise, and Craftbase.

2009: Go

Go was developed by Google to address issues that occur due to large software systems. Due to its simple and modern structure, Go has gained popularity among some of the largest tech companies around the world such as Google, Uber, Twitch, and Dropbox.

2014: Swift

Developed by Apple as a replacement for C, C++, and Objective-C, Swift was developed with the intention to be easier than the aforementioned languages and allow less room for error. Swift's versatility means it can be used for desktop, mobile, and cloud applications. Leading language app 'Duolingo' launched a new app that was written in Swift.

What old programming languages are still used today?

Not all programming languages last forever. Whether it's a result of developments in technology or simply being replaced by a more sophisticated language. With that said, there are some old programming languages that arestill being used by IT professionals today:

FORTRAN

The initial version of FORTRAN was proposed at IBM by John Backus way back in 1953. It was the first optimizer compiler and was widely used for scientific work because it's optimized code competed favorably with hand-written assembly code. The language is still used to this day on some of the world's fastest supercomputers. There have been a string of updates since 1953 with the most recent version released in 2018.

COBOL

COBOL was developed back in 1959 and was one of the first high-level programming languages. Despite being invented so long ago COBOL is still hugely prevalent today. In the US alone, 43% of banking systems are built on COBOL, accounting for 95% of ATM swipes, and 80% of in-person transactions.

Source: Unsplash

Early Microsoft and Apple Programming Languages

Microsoft and Apple are two of the most fundamental companies in the history of programming languages. BASIC (first released in 1964), was modified by Microsoft founders Bill Gates and Paul Allen in 1975. Their modified version became Altair BASIC, which was the first marketed and distributed product of Microsoft, and led to the creation of the company.

Pascal was the programming language of choice during the early days of Apple. Invented in the early 1970s, Apple developers liked Pascal because of its power and ease of use. However, when Apple acquired the NeXTSTEP code library in 1996, Objective-C (which was licensed under NeXTSTEP) became the popular language of choice for the tech giant.

What is the most popular programming language today?

We're nearing the end of our journey through the history of programming languages. As you've seen, computer programming has developed to a level beyond all expectation, powering the technology that we rely on a daily basis. It's for this reason that we need skilled professionals to implement and control the programming languages that power the world today.

But which programming language is the most popular?

The DevSkiller 2020 IT Skills Report provides valuable insight for precisely that question. The report contains data from over 213,000 coding tests sent across 143 countries by companies looking to hire specialized IT professionals.

As you can see above, JavaScript was the most popular language tested on DevSkiller based on the test invites that were sent out. This finding is confirmed in the 2020 Stack Overflow Developer Survey which lists JavaScript as the most popular programming language for the eighth year in a row. SQL, Java, HTML/CSS, and .NET/C# rounded out the top 5 in our results.

Conclusion

The history of programming languages is fascinating. Who would have thought that an algorithm from the mid-19th century would pave the way for the technology-driven society we live in today. From early machine codes to the sophisticated human-readable code that powers our favorite technologies today, programming languages have come a long way. What's for certain, computer programming will continue to develop as it has done for the past 150 years and it's exciting to see what the future brings.

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History of programming languages - DevSkiller

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Computer programming is the very basis of the digital age that we live in today. Every time you like a post on social media, send an email, or set an alarm on your phone, a programming language is working behind the scenes - pulling the strings. But where did it all begin?

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Using Genetic Algorithms to Create the Perfect Keyboard

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Using Genetic Algorithms to Create the Perfect Keyboard

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New Tool Allows Users to See Bias in AI Image Generators

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New Tool Allows Users to See Bias in AI Image Generators

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A new tool is allowing people to see how certain word combinations produce biased results in artificial intelligence (AI) text-to-image generators.

Hosted on Hugging Face, the " Stable Diffusion Bias Explorer " was launched in late October.

According to Motherboard, the simple tool lets users combine descriptive terms and see firsthand how the AI model maps them to racial and gender stereotypes.

What's the difference between these two groups of people? Well, according to Stable Diffusion, the first group represents an 'ambitious CEO' and the second a 'supportive CEO'.
I made a simple tool to explore biases ingrained in this model: https://t.co/l4lqt7rTQj pic.twitter.com/xYKA8w3N8N

- Dr. Sasha Luccioni 💻🌎✨ (@SashaMTL) October 31, 2022

For example, the Stable Diffusion Explorer shows using the word "CEO" on an AI image generator almost always exclusively generates images of men.

However, there is a difference in terms of what the AI image generator thinks an "ambitious CEO" looks like compared to a "supportive CEO." The first description will get the generator to show a diverse host of men in various black and blue suits. The latter descriptor displays an equal number of both women and men.

Pretty cool tool. Well thought out.
The bias is shocking.
Profession: Cook (1st group I picked "self-confident" as the adjective, for the 2nd group I picked compassionate)
In case it is not evident 1st group is all male, 2nd group is all female. https://t.co/RhBjp2V2VP pic.twitter.com/Np67q0YN7R

- deepamuralidhar (@deepamuralidhar) November 1, 2022

Meanwhile, Twitter user @deepamuralidhar showed how the Stable Diffusion Bias Explorer reveals how a "self-confident cook" is depicted as male on AI image generators. Meanwhile a "compassionate cook" is represented as female.

AI ethics researcher for Hugging Face, Sasha Luccioni, developed the tool to show biases in the machine learning models that create images.

"When Stable Diffusion got put up on HuggingFace about a month ago, we were like oh, crap," Luccioni tells Motherboard.

"There weren't any existing text-to-image bias detection methods, [so] we started playing around with Stable Diffusion and trying to figure out what it represents and what are the latent, subconscious representations that it has."

To do this, Luccioni came up with a list of 20 descriptive word pairings. Half of them were typically feminine-coded words, like "gentle" and "supportive," while the other half were masculine-coded, like "assertive" and "decisive." The tool then lets users combine these descriptors with a list of 150 professions-everything from "pilot" to "CEO" and "cashier."

Human Bias

While it is impossible to fully remove human bias from human-made tools, Luccioni believes that tools like the Stable Diffusion Bias Explorer could give users an understanding of the bias in AI systems and could help researchers reverse-engineer these biases.

"The way that machine learning has been shaped in the past decade has been so computer-science focused, and there's so much more to it than that," Luccioni says. "Making tools like this where people can just click around, I think it will help people understand how this works."

The subject of bias in AI image generation systems is becoming increasingly important. In April, OpenAI published a Risks and Limitations document acknowledging that their model can reinforce stereotypes. The document showed how their AI model would generate images for males for prompts like "builder" while the descriptor "flight attendant" would produce female-centric images.
Image credits: Header photo by Stable Diffusion Explorer.

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New Tool Allows Users to See Bias in AI Image Generators

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A new tool is allowing people to see how certain word combinations produce biased results in artificial intelligence (AI) text-to-image generators. Hosted on Hugging Face, the " Stable Diffusion Bias Explorer " was launched in late October. According to Motherboard , the simple tool lets users combine descriptive terms and see firsthand how the AI model maps them to racial and gender stereotypes.

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The Bias in AI Chatbots and How to Avoid it

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The Bias in AI Chatbots and How to Avoid it

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Bias in AI is a very real and prevalent problem, and it often results in malfunctioning chatbots. Here’s an overview of why that happens and how to avoid it.

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OpenAI Chatbot Spits Out Biased Musings, Despite Guardrails

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OpenAI Chatbot Spits Out Biased Musings, Despite Guardrails

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A Brief History of Computer Viruses & What the Future Holds

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A Brief History of Computer Viruses & What the Future Holds

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When it comes to cybersecurity, there are few terms with more name recognition than "computer viruses." Despite the prevalence of these threats and their wide-spread impact, however, many users don't know about the basic nature of viruses. What follows is a brief history of the computer virus, and what the future holds for this widespread cyber threat.

Theory of Self-Replicating Automata

What is a computer virus? This idea was first discussed in a series of lectures by mathematician John von Neumann in the late 1940s and a paper published in 1966, Theory of Self-Reproducing Automata. The paper was effectively a thought experiment that speculated that it would be possible for a "mechanical" organism-such as a piece of computer code-to damage machines, copy itself and infect new hosts, just like a biological virus.

The Creeper Program

As noted by Discovery, the Creeper program, often regarded as the first virus, was created in 1971 by Bob Thomas of BBN. Creeper was actually designed as a security test to see if a self-replicating program was possible. It was-sort of. With each new hard drive infected, Creeper would try to remove itself from the previous host. Creeper had no malicious intent and only displayed a simple message: "I'M THE CREEPER. CATCH ME IF YOU CAN!"

The Rabbit Virus

According to InfoCarnivore, the Rabbit (or Wabbit) virus was developed in 1974, did have malicious intent and was able to duplicate itself. Once on a computer, it made multiple copies of itself, severely reducing system performance and eventually crashing the machine. The speed of replication gave the virus its name.

The First Trojan

Called ANIMAL, the first Trojan (although there is some debate as to whether this was a Trojan, or simply another virus) was developed by computer programmer John Walker in 1975, according to Fourmilab. At the time, "animal programs," which try to guess which animal the user is thinking of with a game of 20 questions, were extremely popular. The version Walker created was in high demand, and sending it to his friends meant making and transmitting magnetic tapes. To make things easier, Walker created PERVADE, which installed itself along with ANIMAL. While playing the game, PREVADE examined all computer directories available to the user and then made a copy of ANIMAL in any directories where it wasn't already present. There was no malicious intent here, but ANIMAL and PREVADE fit the definition of a Trojan: Hiding inside ANIMAL was another program that carried out actions without the user's approval.

The Brain Boot Sector Virus

Brain, the first PC virus, began infecting 5.2" floppy disks in 1986. As Securelist reports, it was the work of two brothers, Basit and Amjad Farooq Alvi, who ran a computer store in Pakistan. Tired of customers making illegal copies of their software, they developed Brain, which replaced the boot sector of a floppy disk with a virus. The virus, which was also the first stealth virus, contained a hidden copyright message, but did not actually corrupt any data.

The LoveLetter Virus

The introduction of reliable, speedy broadband networks early in the 21st century changed the way malware was transmitted. No longer confined to floppy disks or company networks, malware was now able to spread very quickly via email, via popular websites or even directly over the Internet. As a result, modern malware began to take shape. The threat landscape became a mixed environment shared by viruses, worms and Trojans-hence the name "malware" as an umbrella term for malicious software. One of the most serious epidemics of this new era was the LoveLetter, which appeared on May 4, 2000.

As Securelist notes, it followed the pattern of earlier email viruses of the time, but unlike the macro viruses that had dominated the threat landscape since 1995, it didn't take the form of an infected Word document, but arrived as a VBS file. It was simple and straightforward, and since users hadn't learned to be suspicious of unsolicited emails, it worked. The subject line was "I Love You," and each email contained an attachment, "LOVE-LETTER-FOR-YOU-TXT.vbs." The ILOVEYOU creator, Onel de Guzman, designed his worm to overwrite existing files and replace them with copies of itself, which were then used to spread the worm to all the victims' email contacts. Since the message often came to new victims from someone familiar, they were more likely to open it, making ILOVEYOU a proof-of-concept for the effectiveness of social engineering.

The Code Red Virus

The Code Red worm was a "file less" worm-it existed only in memory and made no attempt to infect files on the system. Taking advantage of a flaw in the Microsoft Internet Information Server, the fast-replicating worm wreaked havoc by manipulating the protocols that allow computers to communicate and spread globally in just hours. Eventually, as noted in Scientific American, compromised machines were used to launch a distributed denial of service attack on the Whitehouse.gov website.

Heartbleed

One of the most recent of the major viruses came out in 2014, Heartbleed burst onto the scene and put servers across the Internet at risk. Heartbleed, unlike viruses or worms, stems from a vulnerability in OpenSSL, a general purpose, open source cryptographic library used by companies worldwide. OpenSSL periodically sends out "heartbeats" to ensure that secure endpoints are still connected. Users can send OpenSSL a specific amount of data and then ask for the same amount back-for example, one byte. If users claim they're sending the maximum allowed, 64 kilobytes, but only send a single byte, the server will respond with the last 64 kilobytes of data stored in RAM, notes security technologist, Bruce Schneier, which could include anything from user names to passwords to secure encryption keys.

The Future of Computer Viruses

For more than 60 years, computer viruses have been part of collective human consciousness, however what was once simply cyber vandalism has turned quickly to cybercrime. Worms, Trojans and viruses are evolving. Hackers are motivated and clever, always willing push the boundaries of connection and code to devise new infection methods. The future of cybercrime seems to involve more PoS (point of sale) hacks, and, perhaps, the recent Moker remote access Trojan is a good example of what's to come. This newly-discovered malware is hard to detect, difficult to remove and bypasses all known defenses. Nothing is certain-change is the lifeblood of both attack and defense.

Other helpful reads and links related to Computer Viruses

A Brief History of Computer Viruses & What the Future Holds

When it comes to cybersecurity, there are few terms with more name recognition than "computer viruses." Despite the prevalence of these threats and their wide-spread impact, however, many users don't know about the basic nature of viruses.

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A Brief History of Computer Viruses & What the Future Holds

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When it comes to cybersecurity, there are few terms with more name recognition than "computer viruses." Despite the prevalence of these threats and their wide-spread impact, however, many users don't know about the basic nature of viruses. What follows is a brief history of the computer virus, and what the future holds for this widespread cyber threat.

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Recent improvements in AIs have led to worrying that they could take over the software development industry. But will they have the capability to solve problems as effectively as humans?

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For you

BCS members can read the very latest F-TAG technical briefings and reports.

Yet the legacy of the last forty years means traditional programming skills are still unlikely to disappear in the short term. Java has been the mainstay of enterprise application development for over two decades with millions of lines of code supporting the modern world. Full stack JavaScript powers many websites and mobile applications. Python is hugely popular for machine learning, big data, AI and IoT, and is still the primary language for scientific research.

Is AI engineering better than software engineering?

Instead of AI replacing the software engineer, utilisation of machine learning by skilled engineers will improve the output of software engineering activities by augmenting required practices. These include examples like Co-Pilot from GitHub.

A great example of a current AI tool aimed at developers which can autocomplete lines of code, add whole lines of code, or add entire functions. This will continue to be refined to improve developer productivity and manage away common errors (or mass produce common errors if developed / used badly!).

Another example is the initiative CodeQL - which can effectively give a developer actionable feedback - efficiently finding vulnerabilities in different circumstances.

The use of AI will increase in software engineering across the software development lifecycle to encompass activities such as:

What can AI do in Software Development and Programming?

If we map on a grid how close to usable reality these capabilities are against the impact they could have on the working life of a software engineer we'd see something like this :-

1. Automating code reviews and performance optimisation - using parameters which are machine-learned and avoiding human-driven repetitive regression and performance tests.

2. Improving user experiences by learning how specific users behave and adjusting the user interface adaptively with variable content to reduce customer abandon rates, increase conversion rates and make interfaces more accessible.

3. Automating repetitive DevOps activities around software deployments with a high level of intelligent control to protect against inadvertent mistakes during the deployment process.

4. Improving the approach to security during the development process through automated reviews of code security and assessment against known vulnerabilities. In addition, constant application of security assessment during live use can be a dynamic way of keeping on top of an increasingly critical area of software engineering.

5. Increasingly intelligent software testing capability to drive test execution, qualify and reproduce issues reliably, shortening the development cycle and ensuring higher quality results.

6. Applying AI to the design stage to provide a higher level of direct input when considering the pros and cons of architectural options.

7. Improving estimation accuracy through the application of experience from previous projects, user stories, implementation methods and feature description.

8. Automation of code refactoring when the application of the latest version of a specific technology emerges.

9. Analysis of large system logs to identify and predict degrading issues before they become critical problems and to respond to error situations more intelligently.

10. Improving developer productivity through method recommendation and parameter in-fill and preventing developer syntax errors by integrating AI into the development environment as an IDE (integrated development environment).

11. Improving developer quality by augmenting coding syntax through auto suggestion of how to fulfil a functional requirement and advising on alternative methods which may be better under certain conditions.

12. Database updates / migrations with zero downtime - generally make zero downtime deployments easier to achieve and more mainstream.

13. Automation in process - making developer environments frictionless and easier to identify and rectify vulnerability dependency. Potentially automating the generation of UI from sketches and documentation.

These will all contribute to allowing the software engineer to spend more time on higher level creativity, problem solving and design and do the opposite of removing the need for human software engineering. They make the software engineer ever more valuable.

So, will artificial intelligence replace software engineers?

The short answer is still: 'No'. However, engineers will need to have a broader base of capability as software engineering will become more of a generalist discipline with less routine tasks, fewer segmented specialisms, greater requirement for composable architecture skills, and greater general focus on performance, reliability, sustainability and security over traditional programming.

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As complexity and sophistication grow, software engineers are increasingly assisted by automation tools. Will this trend lead ultimately to AI taking over software engineering? Billy McNeil and Resham Dhillon explore engineering's future.

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Why we are obsessed with the mind-blowing ChatGPT AI  Chatbot

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Why we are obsessed with the mind-blowing ChatGPT AI  Chatbot

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Even if you aren't into artificial intelligence, it's time to pay attention to ChatGPT, because this one is a big deal.

The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT then offers conversational, if somewhat stilted, responses. The bot remembers the thread of your dialogue, using previous questions and answers to inform its next responses. It derives its answers from huge volumes of information on the internet.

ChatGPT is a big deal. The tool seems pretty knowledgeable in areas where there's good training data for it to learn from. It's not omniscient or smart enough to replace all humans yet, but it can be creative, and its answers can sound downright authoritative. A few days after its launch, more than a million people were trying out ChatGPT.

But be careful, OpenAI warns. ChatGPT has all kinds of potential pitfalls, some easy to spot and some more subtle.

"It's a mistake to be relying on it for anything important right now," OpenAI Chief Executive Sam Altman tweeted. "We have lots of work to do on robustness and truthfulness." Here's a look at why ChatGPT is important and what's going on with it.

And it's becoming big business. In January, Microsoft pledged to invest billions of dollars into OpenAI. A modified version of the technology behind ChatGPT is now powering Microsoft's new Bing challenge to Google search and, eventually, it'll power the company's effort to build new AI co-pilot smarts in to every part of your digital life.

Bing uses OpenAI technology to process search queries, compile results from different sources, summarize documents, generate travel itineraries, answer questions and generally just chat with humans. That's a potential revolution for search engines, but it's been plagued with problems like factual errors and and unhinged conversations.

What is ChatGPT?

ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You can ask it countless questions and often will get an answer that's useful.

For example, you can ask it encyclopedia questions like, "Explain Newton's laws of motion." You can tell it, "Write me a poem," and when it does, say, "Now make it more exciting." You ask it to write a computer program that'll show you all the different ways you can arrange the letters of a word.

Here's the catch: ChatGPT doesn't exactly know anything. It's an AI that's trained to recognize patterns in vast swaths of text harvested from the internet, then further trained with human assistance to deliver more useful, better dialog. The answers you get may sound plausible and even authoritative, but they might well be entirely wrong, as OpenAI warns.

Chatbots have been of interest for years to companies looking for ways to help customers get what they need and to AI researchers trying to tackle the Turing Test. That's the famous "Imitation Game" that computer scientist Alan Turing proposed in 1950 as a way to gauge intelligence: Can a human conversing with a human and with a computer tell which is which?

But chatbots have a lot of baggage, as companies have tried with limited success to use them instead of humans to handle customer service work. A study of 1,700 Americans, sponsored by a company called Ujet, whose technology handles customer contacts, found that 72% of people found chatbots to be a waste of time.

ChatGPT has rapidly become a widely used tool on the internet. UBS analyst Lloyd Walmsley estimated in February that ChatGPT had reached 100 million monthly users the previous month, accomplishing in two months what took TikTok about nine months and Instagram two and a half years. The New York Times, citing internal sources, said 30 million people use ChatGPT daily.

What kinds of questions can you ask?

You can ask anything, though you might not get an answer. OpenAI suggests a few categories, like explaining physics, asking for birthday party ideas and getting programming help.

I asked it to write a poem, and it did, though I don't think any literature experts would be impressed. I then asked it to make it more exciting, and lo, ChatGPT pumped it up with words like battlefield, adrenaline, thunder and adventure.

One wacky example shows how ChatGPT is willing to just go for it in domains where people would fear to tread: a command to write " a folk song about writing a rust program and fighting with lifetime errors."

ChatGPT's expertise is broad, and its ability to follow a conversation is notable. When I asked it for words that rhymed with "purple," it offered a few suggestions, then when I followed up "How about with pink?" it didn't miss a beat. (Also, there are a lot more good rhymes for "pink.")

When I asked, "Is it easier to get a date by being sensitive or being tough?" GPT responded, in part, "Some people may find a sensitive person more attractive and appealing, while others may be drawn to a tough and assertive individual. In general, being genuine and authentic in your interactions with others is likely to be more effective in getting a date than trying to fit a certain mold or persona."

You don't have to look far to find accounts of the bot blowing people's minds. Twitter is awash with users displaying the AI's prowess at generating art prompts and writing code. Some have even proclaimed "Google is dead," along with the college essay. We'll talk more about that below.

CNET writer David Lumb has put together a list of some useful ways ChatGPT can help, but more keep cropping up. One doctor says he's used it to persuade a health insurance company to pay for a patient's procedure.

Who built ChatGPT and how does it work?

ChatGPT is the brainchild of OpenAI, an artificial intelligence research company. Its mission is to develop a "safe and beneficial" artificial general intelligence system or to help others do so. OpenAI has 375 employees, Altman tweeted in January. "OpenAI has managed to pull together the most talent-dense researchers and engineers in the field of AI," he also said in a January talk.

It's made splashes before, first with GPT-3, which can generate text that can sound like a human wrote it, and then with DALL-E, which creates what's now called "generative art" based on text prompts you type in.

GPT-3, and the GPT 3.5 update on which ChatGPT is based, are examples of AI technology called large language models. They're trained to create text based on what they've seen, and they can be trained automatically - typically with huge quantities of computer power over a period of weeks. For example, the training process can find a random paragraph of text, delete a few words, ask the AI to fill in the blanks, compare the result to the original and then reward the AI system for coming as close as possible. Repeating over and over can lead to a sophisticated ability to generate text.

It's not totally automated. Humans evaluate ChatGPT's initial results in a process called finetuning. Human reviewers apply guidelines that OpenAI's models then generalize from. In addition, OpenAI used a Kenyan firm that paid people up to $3.74 per hour to review thousands of snippets of text for problems like violence, sexual abuse and hate speech, Time reported, and that data was built into a new AI component designed to screen such materials from ChatGPT answers and OpenAI training data.

ChatGPT doesn't actually know anything the way you do. It's just able to take a prompt, find relevant information in its oceans of training data, and convert that into plausible-sounding paragraphs of text. "We are a long way away from the self-awareness we want," said computer scientist and internet pioneer Vint Cerf of the large language model technology ChatGPT and its competitors use.

Is ChatGPT free?

Yes, for the moment at least, but in January OpenAI added a paid version that responds faster and keeps working even during peak usage times when others get messages saying, "ChatGPT is at capacity right now."

You can sign up on a waiting list if you're interested. OpenAI's Altman warned that ChatGPT's "compute costs are eye-watering" at a few cents per response, Altman estimated. OpenAI charges for DALL-E art once you exceed a basic free level of usage.

But OpenAI seems to have found some customers, likely for its GPT tools. It's told potential investors that it expects $200 million in revenue in 2023 and $1 billion in 2024, according to Reuters.

What are the limits of ChatGPT?

As OpenAI emphasizes, ChatGPT can give you wrong answers and can give "a misleading impression of greatness," Altman said. Sometimes, helpfully, it'll specifically warn you of its own shortcomings. For example, when I asked it who wrote the phrase "the squirming facts exceed the squamous mind," ChatGPT replied, "I'm sorry, but I am not able to browse the internet or access any external information beyond what I was trained on." (The phrase is from Wallace Stevens' 1942 poem Connoisseur of Chaos.)

ChatGPT was willing to take a stab at the meaning of that expression once I typed it in directly, though: "a situation in which the facts or information at hand are difficult to process or understand." It sandwiched that interpretation between cautions that it's hard to judge without more context and that it's just one possible interpretation.

ChatGPT's answers can look authoritative but be wrong.

"If you ask it a very well structured question, with the intent that it gives you the right answer, you'll probably get the right answer," said Mike Krause, data science director at a different AI company, Beyond Limits. "It'll be well articulated and sound like it came from some professor at Harvard. But if you throw it a curveball, you'll get nonsense."

The journal Science banned ChatGPT text in January. "An AI program cannot be an author. A violation of these policies will constitute scientific misconduct no different from altered images or plagiarism of existing works," Editor in Chief H. Holden Thorp said.

The software developer site StackOverflow banned ChatGPT answers to programming questions. Administrators cautioned, "because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking or looking for correct answers."

You can see for yourself how artful a BS artist ChatGPT can be by asking the same question multiple times. I asked twice whether Moore's Law, which tracks the computer chip industry's progress increasing the number of data-processing transistors, is running out of steam, and I got two different answers. One pointed optimistically to continued progress, while the other pointed more grimly to the slowdown and the belief "that Moore's Law may be reaching its limits."

Both ideas are common in the computer industry itself, so this ambiguous stance perhaps reflects what human experts believe.

With other questions that don't have clear answers, ChatGPT often won't be pinned down.

The fact that it offers an answer at all, though, is a notable development in computing. Computers are famously literal, refusing to work unless you follow exact syntax and interface requirements. Large language models are revealing a more human-friendly style of interaction, not to mention an ability to generate answers that are somewhere between copying and creativity.

Will ChatGPT help students cheat better?

Yes, but as with many other technology developments, it's not a simple black-and-white situation. Decades ago, students could copy encyclopedia entries and use calculators, and more recently, they've been able to use search engines and Wikipedia. ChatGPT offers new abilities for everything from helping with research to doing your homework for you outright. Many ChatGPT answers already sound like student essays, though often with a tone that's stuffier and more pedantic than a writer might prefer.

Google programmer Kenneth Goodman tried ChatGPT on a number of exams. It scored 70% on the United States Medical Licensing Examination, 70% on a bar exam for lawyers, nine out of 15 correct on another legal test, the Multistate Professional Responsibility Examination, 78% on New York state's high school chemistry exam's multiple choice section, and ranked in the 40th percentile on the Law School Admission Test.

High school teacher Daniel Herman concluded ChatGPT already writes better than most students today. He's torn between admiring ChatGPT's potential usefulness and fearing its harm to human learning: "Is this moment more like the invention of the calculator, saving me from the tedium of long division, or more like the invention of the player piano, robbing us of what can be communicated only through human emotion?"

Dustin York, an associate professor of communication at Maryville University, hopes educators will learn to use ChatGPT as a tool and realize it can help students think critically.

"Educators thought that Google, Wikipedia, and the internet itself would ruin education, but they did not," York said. "What worries me most are educators who may actively try to discourage the acknowledgment of AI like ChatGPT. It's a tool, not a villain."

Can teachers spot ChatGPT use?

Not with 100% certainty, but there's technology to spot AI help. The companies that sell tools to high schools and universities to detect plagiarism are now expanding to detecting AI, too.

One, Coalition Technologies, offers an AI content detector on its website. Another, Copyleaks, released a free Chrome extension designed to spot ChatGPT-generated text with a technology that's 99% accurate, CEO Alon Yamin said. But it's a "never-ending cat and mouse game" to try to catch new techniques to thwart the detectors, he said.

Copyleaks performed an early test of student assignments uploaded to its system by schools. "Around 10% of student assignments submitted to our system include at least some level of AI-created content," Yamin said.

OpenAI launched its own detector for AI-written text in February. But one plagiarism detecting company, CrossPlag, said it spotted only two of 10 AI-generated passages in its test. "While detection tools will be essential, they are not infallible," the company said.

Researchers at Pennsylvania State University studied the plagiarism issue using OpenAI's earlier GPT-2 language model. It's not as sophisticated as GPT-3.5, but its training data is available for closer scrutiny. The researchers found GPT-2 plagiarized information not just word for word at times, but also paraphrased passages and lifted ideas without citing its sources. "The language models committed all three types of plagiarism, and ... the larger the dataset and parameters used to train the model, the more often plagiarism occurred," the university said.

Can ChatGPT write software?

Yes, but with caveats. ChatGPT can retrace steps humans have taken, and it can generate actual programming code. "This is blowing my mind," said one programmer in February, showing on Imgur the sequence of prompts he used to write software for a car repair center. "This would've been an hour of work at least, and it took me less than 10 minutes."

You just have to make sure it's not bungling programming concepts or using software that doesn't work. The StackOverflow ban on ChatGPT-generated software is there for a reason.

But there's enough software on the web that ChatGPT really can work. One developer, Cobalt Robotics Chief Technology Officer Erik Schluntz, tweeted that ChatGPT provides useful enough advice that, over three days, he hadn't opened StackOverflow once to look for advice.

Another, Gabe Ragland of AI art site Lexica, used ChatGPT to write website code built with the React tool.

ChatGPT can parse regular expressions (regex), a powerful but complex system for spotting particular patterns, for example dates in a bunch of text or the name of a server in a website address. " It's like having a programming tutor on hand 24/7," tweeted programmer James Blackwell about ChatGPT's ability to explain regex.

Here's one impressive example of its technical chops: ChatGPT can emulate a Linux computer, delivering correct responses to command-line input.

What's off limits?

ChatGPT is designed to weed out "inappropriate" requests, a behavior in line with OpenAI's mission "to ensure that artificial general intelligence benefits all of humanity."

If you ask ChatGPT itself what's off limits, it'll tell you: any questions "that are discriminatory, offensive, or inappropriate. This includes questions that are racist, sexist, homophobic, transphobic, or otherwise discriminatory or hateful." Asking it to engage in illegal activities is also a no-no.

Even though OpenAI doesn't want ChatGPT used for malicious purposes, it's easy to use it to write phishing emails to try to fool people into parting with sensitive information, my colleague Bree Fowler reports. "The barrier to entry is getting lower and lower and lower to be hacked and to be phished. AI is just going to increase the volume," said Randy Lariar of cybersecurity company Optiv.

Is this better than Google search?

Asking a computer a question and getting an answer is useful, and often ChatGPT delivers the goods.

Google often supplies you with its suggested answers to questions and with links to websites that it thinks will be relevant. Often ChatGPT's answers far surpass what Google will suggest, so it's easy to imagine GPT-3 is a rival.

But you should think twice before trusting ChatGPT. As when using Google and other sources of information like Wikipedia, it's best practice to verify information from original sources before relying on it.

Vetting the veracity of ChatGPT answers takes some work because it just gives you some raw text with no links or citations. But it can be useful and in some cases thought provoking. You may not see something directly like ChatGPT in Google search results, but Google has built large language models of its own and uses AI extensively already in search.

That said, Google is keen to tout its deep AI expertise, ChatGPT triggered a "code red" emergency within Google, according to The New York Times, and drew Google co-founders Larry Page and Sergey Brin back into active work. Microsoft could build ChatGPT into its rival search engine, Bing. Clearly ChatGPT and other tools like it have a role to play when we're looking for information.

So ChatGPT, while imperfect, is doubtless showing the way toward our tech future.
Editors' note: CNET is using an AI engine to create some personal finance explainers that are edited and fact-checked by our editors. For more, see .

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Why We're Obsessed With the Mind-Blowing ChatGPT AI Chatbot

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Even if you aren't into artificial intelligence, it's time to pay attention to ChatGPT, because this one is a big deal. The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT then offers conversational, if somewhat stilted, responses.

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100 Days of Code - Learn Python on Replit

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100 lessons in 100 days, to master Python and become a developer. Learn how to code with an interactive online course, for free, starting from scratch.

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Build and deploy software collaboratively with the power of AI without spending a second on setup.

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The Greatest Technological advancements of the 21st Century

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The Greatest Technological advancements of the 21st Century

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We have never lived in a world of faster and more wide-reaching technology innovations. Our jobs, businesses, and how we operate as societies are being transformed by technology, and the current global pandemic is only fast-tracking the digital transformation. With this post, I want to delve into the top 10 tech trends that are driving the 4th Industrial Revolution, and that will define the next decade.

Here Are The Top 10 Technology Trends Of The 4Th Industrial Revolution
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1. Artificial Intelligence & Machine Learning
What it is: Artificial intelligence (AI) and machine learning refer to the ability of machines to learn and act intelligently-meaning they can make decisions, carry out tasks and even predict future outcomes based on what they learn from the data.

I believe AI will transform our world and how we live in it. It's already used in our everyday lives from Google searches to powering Amazon's product recommendations and the personalized suggestions you get from Netflix and Spotify as well as in the security process for fraudulent credit card use. AI and machine learning are also the foundation of which many other technology trends are built. AI gives machines the ability to carry out a wide range of human-like processes, such as seeing (facial recognition), writing (chatbots), and speaking (Alexa). AI will infiltrate even more of our lives as the ability for machines to act intelligently gets better and better.
2. The Internet of Things (IoT)
What it is: The Internet of Things (IoT) refers to the increasing number of everyday devices and objects that are connected to the internet and gather and transmit data.

While the first smart device many of us were exposed to was a smartphone, we now have smartwatches, TVs, refrigerators, and will soon have smart everything-these days, everything is getting smarter. Today there are 20 billion smart devices, but it is expected to grow to at least 200 billion connected devices soon. These smart devices are responsible for an explosion of data and are rapidly changing our world and the way we live in it. The ability of machines to connect to and share information with each other is a key part of the IoT.
3. Big Data
What it is: In simple terms, "big data" refers to the exponential explosion in the amount of data being generated in the increasingly digital age.

Our world is full of data, more than ever before. The more data you have, the easier it is to gain new insights and even predict what will happen in the future. By analyzing masses of data with intelligent algorithms, it's possible to spot patterns and relationships that were previously unknown. And when you can understand the relationships between data points, you can better predict future outcomes and make smarter decisions about what to do next. Augmented insights that can extract insights automatically will give companies unprecedented insights from their data.
4. Blockchains
What it is: A blockchain or distributed ledger is, in simplistic terms, a kind of highly secure database, a way of storing information.

In today's digital age, storing, authenticating, and protecting data presents serious challenges for many organizations. Blockchain technology, a form of open, distributed ledger, promises a practical and super secure solution to this problem. As a result, blockchain is an increasingly attractive tool for industries such as banking and insurance. It will transform how banks operate and how we keep our possessions.
5. Cloud & Edge Computing
What it is: Cloud computing means storing and processing data on other people's computers in a data center via a network that gives companies the ability to store massive amounts of data and process it in nearly real-time. Edge computing refers to the processing of data on devices such as smartphones.

Cloud service providers such as Amazon, Google, and Microsoft allow companies to host all vital IT infrastructure in their cloud rather than within an organization's digital walls reducing overhead costs to maintain and operate individual systems, software and data. Edge sits at the other end of the scale-rather than far away in remote data centers, edge computing happens up-close-and-personal on the frontline of business operations. Rather than send every piece of information collected by cameras, scanners, handheld terminals or sensors to the cloud to be processed, edge devices carry out some or all of the processing themselves, at the source where the data is collected.
6. Robots & Cobots
What it is: Today's robots can be defined as intelligent machines that can understand and respond to their environment and perform routine or complex tasks autonomously.

In this data-driven age, it's the intelligence and ability to act autonomously that defines robots and separates them from other machines. The rise of collaborative robots, or cobots, is the latest generation of robotic systems designed to work alongside humans as robotic colleagues. Cobots enhance the work that humans do and interact safely and easily with the human workforce-extra robotic muscle in the workplace.
7. Autonomous Vehicles
What it is: An autonomous vehicle-be it a car, truck, ship, or other vehicle-is one that can sense what's going on around it and operate without human involvement.

Every major car manufacturer is investing heavily in self-driving technology, and self-driving vehicles could change the face of our cities. They potentially will reduce pollution, drastically improve the daily commute, and more.
8. The 5G Network
What it is: 5G is the fifth generation of cellular network technology, which, together with other network innovations, will give us much faster and more stable wireless networking, as well as the ability to connect more and more devices and enabling richer, more varied streams of data.

Networking technology is the backbone of our online society and a smarter world. As bandwidth and coverage have increased, more has become possible from email to web browsing, location-based services, and streaming video and games. We send a constant stream of real-time data back and forth between ourselves, the apps and devices we use, and the cloud services which power them. The 5G network will give us not only greater speeds but will also be able to cope with connecting far more devices within a geographical area.
9. Genomics & Gene Editing
What it is: Genomics is an interdisciplinary field of biology that focuses on the understanding and manipulation of DNA and genomes of living organisms. Gene editing is a group of technologies that enables genetic engineering in order to change the DNA and genetic structure of living organisms.

Increasingly powerful computers and sophisticated software tools now available were instrumental in helping us understand the human genome since it was first accurately sequenced in 2003. Today, biotechnology has advanced to the point where it's viable to alter the DNA encoded within a cell and influence the characteristics that its descendants will have after it reproduces by cell division.
10. Quantum Computing
What it is: Quantum computing harnesses the peculiar phenomenon observed to take place when operating at a subatomic level, which includes quantum entanglement, quantum tunneling, and the ability of quantum particles to simultaneously exist in more than one state.

Quantum computing will completely redefine what a computer is and could give us computing power that is millions or trillions times more powerful than supercomputers today. Although conventional binary computing is likely to be all we will need for many tasks we carry out on computers in the near future, incomprehensibly quick quantum computing is likely to have a variety of applications in fields such as artificial intelligence and decoding complex structures such as genomic information.

Read more about these key technology trends - including practical examples from a wide range of industries - in my new book, Tech Trends in Practice: The 25 Technologies That Are Driving The 4th Industrial Revolution.

... Read More

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The Top 10 Technology Trends Of The 4th Industrial Revolution

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There are many technology trends that are currently altering the world as we know it. Here I pull out the top 10 future tech trends that will define the next decade and explain what they are and how each is transforming our world.

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Managing the risks and rewards of emerging technologies is a tricky balancing act. How is it possible to maximise the upsides of innovation while minimising the potential downsides?

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New technology: what do you have to fear?